Relationship between pixels, image acquisition system & Imaging Geometry

Jun 18 • Notes • 13166 Views • 27 Comments on Relationship between pixels, image acquisition system & Imaging Geometry

Basic Relationships Between Pixels

1. Neighborhood

2.Adjacency

3. Connectivity

4.Paths

5.Regions and boundaries

1.Neighbors of a Pixel

• Any pixel p(x, y) has two vertical and two horizontal neighbors, specified by

[(x+1, y), (x-1, y), (x, y+1), (x, y-1)]

• This set of pixels are known the 4-neighbors of P, and is denoted by N4(P).

•Aall of them are at a unit distance from P.

• The four diagonal neighbors of p(x,y) are given by, [(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1 ,y-1)]

• This set is denoted by ND (P).

• Each of them are at Euclidean space of 1.414 from P.

• The points ND(P) and N4 (P) are together known as 8-neighbors of the point P, denoted by N8(P).

• Some of the points in the N4, ND and N8may fall outside image when P lies on the border of image.

Neighbors of a pixel

a. 4-neighbors of a pixel p are its vertical and horizontal neighbors denoted by N4(p)

b. 8-neighbors of a pixel p are its vertical horizontal and 4 diagonal neighbors denoted by N8(p)

2.Adjacency

• Two pixels are linked if they are neighbors and their gray levels satisfy few detailed pattern of similarity.

• For instance, in a binary image two pixels are connected if they are 4-neighbors and have same value (0/1).

PATHS

  • A path from pixel p with coordinates (x, y) to pixel q with coordinates (s,t) is a sequence of distinct pixel with coordinates:

(x0, y0), (x1, y1), (x2, y2)……… (xn, yn), where  (x0, y0) = (x, y) and  (xn, yn) =  (s, t); (xi, yi) is adjacent to (xi-1, yi-1) for 1≤i ≤ n . where n is the length of path.

 

REGION AND BOUNDARIES

A subset R of pixel in an image is called a region of the image R in a connected set. The boundary of the region  R is the set of pixel in the region that have one or more neighbors that are not in R.

Imaging Geometry

Image Geometry Correction (frequently referred to as Image Warping) is the procedure of digitally manipulating image data such that the image’s projection accurately matches a specific projection surface or shape. Image geometry correction balances the deformation formed by off-axis projector or screen mangement or asymmetrical screen surface, by apply a pre-compensating inverse distortion to that image in the digital domain.

 

Image Acquisition Systems

Goal:

To capture and record localized information about the physical and/or functional properties of tissues or cells.

  •  Faithfulness and Efficiency of imaging is vital

Rrepetitive advances in imaging technology

  • Routine clinical capability in 3D acquisition and 4D (fast scanning in 3D)
  • 3D volume acquirement leverages the importance of 3D biomedical visualization and analysis techniques
  • A wealth of 3D multimodal image information – leads to an integrated synergistic paradigm for improved diagnosis/treatment.

Image Acquisition Fundamentals: Image Formation

  • Some form of energy is find after passage through and interaction with a region of the body.
  • Mathematical estimates are computed and images formed (of 2D/3D distributions of energy interactions with body tissues)
  • Typically, simultaneous measurements are prepared of different regions of the body.
  • Types of energy interactions: attenuation, combination, magnetic resonance, etc.
  • Image Acquisition Fundamentals: Spatial Resolution
  • Images are discrete – a pixel or voxel measures a specific sized region of the body.
  • Dimensions determined by “space resolving power” of the acquisition system.
  •  as well a function of image processing/reconstruction
  • Limits of the spatial resolution are the least object dimensions
  •  Isotropic vs. non-isotropic

Image Acquisition Fundamentals: Contrast Resolution

  • Individual structures familiar by localized differences in signal strength
  • Visibility depends on “contrast” in opposition to surrounding structures
  • Contrast resolution: capability to detect differences in intensities between two structures – depends on physical properties of imaged structure
  • Specified as a percent biggest signal difference
  • Image Acquisition Fundamentals Temporal Resolution
  •  Has 2 definitions: “aperture time” and “frame rate”
  • Aperture Time: ◦ Time taken to capture the signal information to form a sole image
    • vital to the temporal resolving power of the imaging system
    • Key to eliminate motion artifact.
    • Frame Rate:
    • Small interval of time taken between successive images
    • Does not comprise reconstruction time, but the time required to reset and acquire signal information for a new image.
    •  vital to acquiring 4D volumes, may require “gating” of physiological events (cardiac cycle).

 

 

QUESTION & ANSWER

  1. Relationship between pixels. Explain??

ANSWER

1. Neighborhood

2.Adjacency

3. connectivity

4.Paths

5.Regions and boundaries

2. What is N4(P) and ND(P)??

ANSWER

Any pixel p(x, y) has two vertical and two horizontal neighbors, specified by,

[(x+1, y), (x-1, y), (x, y+1), (x, y-1)].  This set of pixel is known as  4-neighbor of P, and is denoted by N4(P).

The four diagonal neighbors of p(x,y) are given by, [(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1 ,y-1)].  This set is denoted by ND (P).

3. What is image geometry image correction??

ANSWER

Image Geometry Correction is the procedure of digital manipulating image data such that the image’s projection accurately matches specific projection surface or shape. Image geometry correction  compensate for the distortion formed by off-axis projector or screen placement or non-flat screen surface, by apply a precompensating inverse distortion to that image in the digital domain.

4. What is image acquisition system??

ANSWER

To capture and record localized information about the physical and/or functional properties of tissues or cells.

  • Faithfulness and Efficiency of imaging is vital Repetitive advances in imaging technology.
  • Routine clinical capability in 3D acquisition and 4D (fast scanning in 3D).
  • 3D volume acquirement leverages the importance of 3D biomedical visualization and analysis techniques.
  • A wealth of 3D multimodal image information – leads to an integrated synergistic paradigm for improved diagnosis/treatment.

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27 Responses to Relationship between pixels, image acquisition system & Imaging Geometry

  1. Pushpendra says:

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  10. Mohit vishwakarm says:

    Maths

  11. Rahul says:

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  14. ch seetha ram says:

    i need to know about pixel relationship

  15. ch seetha ram says:

    i need to know about pixel relationship

  16. ch seetha ram says:

    i need to know about pixel relationship

  17. ch seetha ram says:

    i need to know about pixel relationship

  18. ch seetha ram says:

    i need to know about pixel relationship

  19. ch seetha ram says:

    i need to know about pixel relationship

  20. ch seetha ram says:

    i need to know about pixel relationship

  21. R.Ramya Shamini Mol says:

    applications and future purposes of relatinship between pixels

  22. sunny says:

    really this is topic about image processing and sounds good

  23. patlakshi says:

    This post comprises about the relationship between pixels and imaging geometry. This is the topic related to digital image processing.

  24. Seetha Manoharan says:

    most of us know how to take picture. but they doesnt know the process on it. this article is helpful to know the part of image.

  25. Dinesh says:

    This is a well composed article which gives the answer for everyone to “how this image geometry created using pixels”. This will raise for everyone who study about pixels.

  26. Madhan says:

    Detailed explanation about the pixel and imaging geometry and the relationship between this.

  27. SNEHA SINHA says:

    An informative piece of article worth a read.. dealing with relationship between pixels and imaging geometry

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